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Modeling Latent Sentence Structure in Neural Machine Translation ...
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Deep Generative Model for Joint Alignment and Word Representation ...
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Multi30K: Multilingual English-German Image Descriptions ...
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Statistical machine translation for automobile marketing texts
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In: Läubli, Samuel; Fishel, Mark; Weibel, Manuela; Volk, Martin (2013). Statistical machine translation for automobile marketing texts. In: Proceedings of the XIV Machine Translation Summit, Nice, 2 September 2013 - 6 September 2013, 265-272. (2013)
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A syntactified direct translation model with linear-time decoding
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In: Hassan, Hany, Sima'an, Khalil and Way, Andy orcid:0000-0001-5736-5930 (2009) A syntactified direct translation model with linear-time decoding. In: EMNLP 2009 - Conference on Empirical Methods in Natural Language Processing, 6-7 August 2009, Singapore. (2009)
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Lexical syntax for statistical machine translation
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Hassan, Hany. - : Dublin City University. School of Computing, 2009
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In: Hassan, Hany (2009) Lexical syntax for statistical machine translation. PhD thesis, Dublin City University. (2009)
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Lexicalized semi-incremental dependency parsing
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In: Hassan, Hany, Sima'an, Khalil and Way, Andy orcid:0000-0001-5736-5930 (2009) Lexicalized semi-incremental dependency parsing. In: RANLP-2009 - Recent Advances in Natural Language Processing, 14-16 September 2009, Borovets, Bulgaria. (2009)
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Supertagged phrase-based statistical machine translation
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In: Hassan, Hany, Sima'an, Khalil and Way, Andy orcid:0000-0001-5736-5930 (2007) Supertagged phrase-based statistical machine translation. In: ACL 2007 - 45th Annual Meeting of the Association for Computational Linguistics, 25-27 June 2007, Prague, Czech Republic. (2007)
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Parsing Arabic Dialects
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Habash, Nizar Y.; Rambow, Owen C.; Chiang, David; Diab, Mona T.; Hwa, Rebecca; Sima'an, Khalil; Lacey, Vincent; Levy, Roger; Nichols, Carol; Shareef, Safiullah. - : Technical report, JHU SummerWorkshop, 2006
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Abstract:
The Arabic language is a collection of spoken dialects with important phonological, morphological, lexical, and syntactic differences, along with a standard written language, Modern Standard Arabic (MSA). Since the spoken dialects are not officially written, it is very costly to obtain adequate corpora to use for training dialect NLP tools such as parsers. In this paper, we address the problem of parsing transcribed spoken Levantine Arabic (LA). We do not assume the existence of any annotated LA corpus (except for development and testing), nor of a parallel corpus LA-MSA. Instead, we use explicit knowledge about the relation between LA and MSA.
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Keyword:
Computer science; Information technology
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URL: https://doi.org/10.7916/D85T3TZG
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